1. health and fitness
2. disease
3. cancer

AJCN. First published ahead of print January 14, 2015 as doi: 10.3945/ajcn.114.100065.
Physical activity and all-cause mortality across levels of overall and
abdominal adiposity in European men and women: the European
Prospective Investigation into Cancer and Nutrition Study (EPIC)1–6
Ulf Ekelund, Heather A Ward, Teresa Norat, Jian’an Luan, Anne M May, Elisabete Weiderpass, Stephen S Sharp,
Kim Overvad, Jane Nautrup Østergaard, Anne Tjønneland, Nina Føns Johnsen, Sylvie Mesrine, Agn
es Fournier, Guy Fagherazzi,
Antonia Trichopoulou, Pagona Lagiou, Dimitrios Trichopoulos, Kuanrong Li, Rudolf Kaaks, Pietro Ferrari, Idlir Licaj,
Mazda Jenab, Manuela Bergmann, Heiner Boeing, Domenico Palli, Sabina Sieri, Salvatore Panico, Rosario Tumino,
Paolo Vineis, Petra H Peeters, Evelyn Monnikhof, H Bas Bueno-de-Mesquita, J Ram
on Quir
os, Antonio Agudo,
María-Jos
e S
anchez, Jos
e María Huerta, Eva Ardanaz, Larraitz Arriola, Bo Hedblad, Elisabet Wirf€
alt, Malin Sund,
Mattias Johansson, Timothy J Key, Ruth C Travis, Kay-Tee Khaw, Søren Brage, Nicholas J Wareham, and Elio Riboli
ABSTRACT
Background: The higher risk of death resulting from excess adiposity may be attenuated by physical activity (PA). However, the theoretical number of deaths reduced by eliminating physical inactivity
compared with overall and abdominal obesity remains unclear.
Objective: We examined whether overall and abdominal adiposity
modified the association between PA and all-cause mortality and estimated the population attributable fraction (PAF) and the years of life
gained for these exposures.
Design: This was a cohort study in 334,161 European men and
women. The mean follow-up time was 12.4 y, corresponding to
4,154,915 person-years. Height, weight, and waist circumference
(WC) were measured in the clinic. PA was assessed with a validated self-report instrument. The combined associations between
PA, BMI, and WC with mortality were examined with Cox proportional hazards models, stratified by center and age group, and
adjusted for sex, education, smoking, and alcohol intake. Centerspecific PAF associated with inactivity, body mass index (BMI; in
kg/m2) (.30), and WC ($102 cm for men, $88 cm for women)
were calculated and combined in random-effects meta-analysis.
Life-tables analyses were used to estimate gains in life expectancy
for the exposures.
Results: Significant interactions (PA 3 BMI and PA 3 WC) were
observed, so HRs were estimated within BMI and WC strata. The hazards of all-cause mortality were reduced by 16–30% in moderately inactive individuals compared with those categorized as inactive in
different strata of BMI and WC. Avoiding all inactivity would theoretically reduce all-cause mortality by 7.35% (95% CI: 5.88%, 8.83%).
Corresponding estimates for avoiding obesity (BMI .30) were 3.66%
(95% CI: 2.30%, 5.01%). The estimates for avoiding high WC were
similar to those for physical inactivity.
Conclusion: The greatest reductions in mortality risk were observed
between the 2 lowest activity groups across levels of general and
abdominal adiposity, which suggests that efforts to encourage even
small increases in activity in inactive individuals may be beneficial to
public health.
Am J Clin Nutr doi: 10.3945/ajcn.114.100065.
Keywords cohort study, epidemiology, obesity, physical activity,
exercise, mortality, population attributable fraction
INTRODUCTION
Physical inactivity has been consistently associated with an
increased risk of all-cause mortality independent of general
adiposity defined by BMI (1–3). Studies that have examined the
1
From the Medical Research Council (MRC) Epidemiology Unit, University
of Cambridge, United Kingdom (UE, JL, SSS, SB, and NJW); the Department
of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway (UE);
Imperial College, London, United Kingdom (HAW, TN, PV, HBB-d-M, and
ER; University Medical Centre Utrecht, Julius Centre for Health Sciences and
Primary Care, Utrecht,The Netherlands (AMM, PHP, and EM); the Department
of Community Medicine, Faculty of Health Sciences, University of Tromsø,
Tromsø, Norway (EW); the Department of Research, Cancer Registry of Norway, Oslo, Norway (EW); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (EW); Samfundet
Folkh€alsan, Helsinki, Finland (EW); the Section for Epidemiology, Department
of Public Health, Aarhus University, Aarhus, Denmark (KO and JNØ); the
Department of Cardiology, Center for Cardiovascular Research, Aalborg University Hospital, Aalborg, Denmark (KO and JNØ); Danish Cancer Society,
Copenhagen, Denmark (A Tjønneland and NFJ; Inserm, Centre for Research in
Epidemiology and Population Health, Nutrition, Hormones and Women’s
Health team, Villejuif, France (SM, AF, and GF); the Univeristy of Paris
Sud, UMRS 1018, Villejuif, France (SM, AF, and GF); IGR, Villejuif, France
(SM, AF, and GF); WHO Collaborating Center for Food and Nutrition Policies,
Department of Hygiene, Epidemiology and Medical Statistics, University of
Athens Medical School, Athens, Greece (A Trichopoulou and PL); Hellenic
Health Foundation, Athens Greece (A Trichopoulou and DT); the Department
of Epidemiology, Harvard School of Public Health, Boston, MA (PL and DT);
the Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece
(PL and DT); the Division of Cancer Epidemiology, German Cancer Research
Centre, Heidelberg, Germany (KL and RK); International Agency for Research
on Cancer (IARC), Lyon, France (PF, IL, and MJ); the Department of Epidemiology, Deutsches Institut fu¨r Ern€ahrungsforschung, Potsdam-Rehbru¨cke, Germany (MB and HB); Molecular and Nutrional Epidemiology Unit, ISPO,
Cancer Prevention and Research Institute, Florence, Italy (DP); Epidemiology
and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano,
Italy (SS); Dipartimento di Medicina Clinica e Chirurgia, Federico ii University,
Naples, Italy (SP); UOS Registro Tumori e UOC Anatomia Patologica, Ospedale “Civile MP Arezzo” ASP 7, Ragusa, Italy (RT); HuGEF Foundation, Turin,
Italy (PV); National Institute for Public Health and the Environment, Bilthoven,
The Netherlands (HBB-d-M); the Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands (HBB-d-M); Public Health Directorate, Asturias, Spain (JRQ); Unit of Nutrition, Environment
and Cancer, Cancer Epidemiology Research Program, Catalan Institute of
Am J Clin Nutr doi: 10.3945/ajcn.114.100065. Printed in USA.
Copyright (C) 2015 by the American Society for Nutrition
1 of 9
2 of 9
EKELUND ET AL.
combined associations between physical activity (PA),7 BMI,
and mortality suggest that PA protects again premature death but
does not eliminate the increased risk associated with high BMI
(4–8). However, these previous examinations of the combined
association between PA and obesity with mortality have relied
on self-reported anthropometric data (5–7), have been restricted
to single sex cohorts (6, 8), and have included only small
numbers of deaths (8, 9), and few studies have examined PA
combined with both BMI and waist circumference (WC) in relation to mortality (5, 8, 9). Furthermore, those that have examined the combined associations between PA, adiposity, and
mortality have used a dichotomous categorization of PA and
BMI (9), leaving uncertainty about whether PA protects against
premature deaths across established BMI and WC categories
(10, 11).
Whereas it could be hypothesized that PA exerts its influence
on mortality indirectly through reducing adiposity, recent data
from the European Prospective Investigation into Cancer and
Nutrition (EPIC) suggest that PA is unrelated to change in body
Oncology, Barcelona, Spain (AA); Andalusian School of Public Health, Granada, Spain (M-JS); CIBER de Epidemiología y Salud Pu´blica (CIBERESP),
Spain (M-JS, JMH, and EA); the Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain (JMH); Navarre Public Health Institute,
Pamplona, Spain (EA); Public Health Division of Gipuzkoa, Instituto BIODonostia, Basque Government, CIBER Epidemiología y Salud Pu´blicaCIBERESP, Spain (LA); Cardiovascular Epidemiology (BH) and Nutritional
Epidemiology (EW), Department of Clinical Sciences, Lund University,
Malmo¨, Sweden; the Department of Public Health and Clinical Medicine,
Umea˚ University, Umea˚, Sweden (MS and MJ); Cancer Epidemiology Unit,
Nuffield Department of Clinical Medicine, University of Oxford, Oxford,
United Kingdom (TJK and RCT); and Clinical Gerontology Unit, University
of Cambridge, Cambridge, United Kingdom (K-TK).
2
The EPIC is supported by grants from the European Commission: Public
Health and Consumer Protection Directorate 1993–2004; Research DirectorateGeneral 2005–present; Deutsche Krebshilfe; German Cancer Research Center; German Federal Ministry of Education and Research; Danish Cancer
Society; Health Research Fund of the Spanish Ministry of Health (Network
of Centers of Research in Epidemiology and Public Health C03/09); the
Spanish Regional Governments of Andalucia, Asturias, Basque Country,
Murcia, and Navarra; Cancer Research United Kingdom; Medical Research
Council, United Kingdom; the Stroke Association, United Kingdom; British
Heart Foundation; Department of Health, United Kingdom; Food Standards
Agency, United Kingdom; the Wellcome Trust, United Kingdom; Greek Ministry of Health and Social Solidarity and Hellenic Health Foundation; Greek
Ministry of Education; Italian Association for Research on Cancer; Dutch Ministry of Public Health, Welfare, and Sports; National Cancer Registry and the
Regional Cancer Registries Amsterdam, East, and Maastricht of the Netherlands; World Cancer Research Fund; Statistics Netherlands; Swedish Cancer
Society; Swedish Scientific Council; Regional Government of Ska˚ne, Sweden;
French League Against Cancer; the 3M Company; Mutuelle Generale de l’Education Nationale, France; Institut Gustave Roussy, France; and Institut National
de la Sante et de la Recherche Medicale, France. UE, JL, SSS, SB, and NJW
were funded by the MRC Epidemiology Unit Programmes (MC_UU_12015/1
and MC_UU_12015/4). This is an open access article distributed under the
CC-BY license (http://creativecommons.org/licenses/by/3.0/).
3
Supplemental Tables 1–7 are available from the “Supplemental data” link in
the online posting of the article and from the same link in the online table of
contents at http://ajcn.nutrition.org.
4
UE and HAW are joint first authors.
5
NJW and ER contributed equally to this work.
6
Address correspondence to U Ekelund, MRC Epidemiology Unit, University
of Cambridge, Box 285, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2
0QQ, United Kingdom. E-mail: ulf.ekelund@mrc-epid.cam.ac.uk.
7
Abbreviations used: EPIC, European Prospective Investigation into Cancer
and Nutrition; PA, physical activity; PAEE, physical activity energy expenditure; PAF, population attributable fraction; WC, waist circumference.
Received September 24, 2014. Accepted for publication December 12, 2014.
doi: 10.3945/ajcn.114.100065
weight and inversely, albeit weakly, associated with change in
WC (12). Thus, PA may interact differentially with BMI and WC
in relation to all-cause mortality.
We therefore examined the associations between PA and allcause mortality and whether BMI and WC modified these
associations in a large sample of 334,161 men and women
followed for .12 y from the EPIC study, in which both BMI
and WC were measured during clinical examinations at baseline. As a secondary aim we estimated how many deaths could
theoretically be avoided if inactive or obese individuals were
more active or nonobese, respectively, and calculated the
years of gain in life expectancy from avoiding physical inactivity, high BMI (in kg/m2; $30), and high WC (.88 cm
and .102 cm in women and men), separately and combined
in the cohort.
SUBJECTS AND METHODS
The EPIC cohort
EPIC is a multicenter prospective cohort study, which
recruited 519,978 volunteers from 23 centers in 10 countries
[Sweden, Denmark, Norway, The Netherlands, United Kingdom, France, Germany, Spain, Italy, and Greece] between 1992
and 2000. The study population included volunteers aged
mostly 25–70 y at the time of recruitment and has been described in detail previously (13, 14). There were 518,408
participants for whom vital statistics were available at the end
of follow-up (2010). Individuals who reported either baseline
heart disease (n = 6256), stroke (n = 3485), cancer (n =
15,926) or a combination of these conditions (n = 1092) were
excluded from the analysis. Participants who were missing
data on PA were excluded (n = 45,725); this included the entire
cohort from Norway (n = 36,920) as the information collected
on leisure-time PA was not compatible with the other EPIC
centers questionnaires. Participants in the top and bottom
0.5th percentile of the energy intake to estimated basal metabolic rate ratio were excluded (n = 8637) because of unrealistic dietary intake information, as were those with
missing data on the following covariates: height and weight
(n = 54,522), WC (n = 28,548), alcohol (n = 478), education
(n = 12,276), and smoking (n = 3031). Participants with extreme anthropometric measurements (height ,130 cm, weight
.250 kg, BMI ,18.5, WC ,40 cm, WC .160 cm, or BMI
.25 and WC ,60 cm) were additionally excluded (n = 4271).
The current analysis therefore included all participants (n =
334,161) for whom measured height, weight, and WC were
available. A detailed comparison between excluded and included participants is available elsewhere (Supplemental
Table 1).
To maintain sample size per center without combining groups
considered to be too heterogeneous with respect to lifestyle
characteristics, the 23 EPIC centers were analyzed as the following 11 groups in the current analysis: France, Italy, Spain,
United Kingdom health conscious (Oxford participants recruited
through the Vegetarian Society), United Kingdom general (all
United Kingdom participants not included in the Health Conscious
group), The Netherlands, Greece, Heidelberg, Potsdam, Sweden,
and Denmark (14). The study was approved by the Institutional
Review Board at the International Agency for Research on Cancer
PHYSICAL ACTIVITY AND MORTALITY
and local ethics committees, and informed consent forms were
signed at each local center.
Assessment of physical activity
Data on occupational, recreational, and household PA during
the past year either were obtained from an in-person interviews or
were self-administered by using a standardized questionnaire.
Participants reported their level of occupational PA as either
sedentary (e.g., office work), standing (e.g., hairdresser, guard),
physical work (e.g., plumber, nurse), or heavy manual work (e.g.,
construction worker, bricklayer). The Cambridge Index of PA
was derived by combining occupational activity level with recreational activity, as assessed by the amount of time in hours per
week during winter and summer spent cycling and in other
physical exercises (e.g., jogging, swimming) and is summarized
into 4 groups: active, moderately active, moderately inactive, and
inactive (15, 16).
We examined the validity of the Cambridge Index in an independent substudy in 1941 men and women similar in age to
those in the original EPIC cohort, across all 10 EPIC countries by
using combined movement and heart rate monitoring as the
criterion (16). PA energy expenditure (PAEE) increased significantly by increasing categories of self-reported PA (P-trend ,
0.0001), with a significant correlation between measured PAEE
and the categorical PA index (Spearman correlation = 0.33, P ,
0.013). Further calibration results suggest that the average PAEE
across categories of PA were as follows: inactive (36 kJ/kg daily);
moderately inactive (41 kJ/kg daily); moderately active (46 kJ/kg
daily), and active (51 kJ/kg daily). The format of the PA questions was somewhat different in Naples (Italy) relative to the
other centers in the current analysis; however, the data from
Naples were transformed for inclusion in the PA index. In Umea˚
(Sweden), a 4-level PA variable was derived from cross-tabulation
of occupational and exercise; owing to the similarity with the
Cambridge categories (inactive, moderately inactive, moderately
active, active) (16), the Umea˚ classification has been incorporated
into the current analysis.
Assessment of anthropometric measures
Body weight (in kg) and height (in cm) were measured at
baseline according to standardized procedures without shoes
(17). Self-reported anthropometric data (Oxford) were adjusted
by using prediction equations derived from the general population, where a subset of participants had both self-reported and
measured anthropometric data available (17, 18). WC (in cm)
was measured at the narrowest torso circumference or at the
midpoint between the lower ribs and iliac crest. Weight measurements were corrected to account for protocol differences
between centers as previously described (18). BMI was calculated as body weight (in kg) divided by height squared (in m).
Individuals were categorized into normal-weight (BMI: 18.5–
24.9), overweight (BMI: 25–30), and obese (BMI $30). Participants were dichotomized by WC values by using the cutoffs
of $102 cm among men and $88 cm among women.
Assessment of endpoints
Mortality data were obtained at the regional or national
level. In Denmark, Italy, The Netherlands, Spain, Sweden, and
3 of 9
the United Kingdom, vital status and the causes and the dates of
death were ascertained by death indexes, cancer registry records,
and national health statistics. Active follow-up was adopted in
Germany, Greece, and France. Causes of death were coded
according to the International Classification of Diseases, 10th
Revision (19). The endpoint in the current analysis was death
from all causes collected between 2008 and 2010, depending on
the center.
Statistical analysis
We examined the associations between PA, adiposity, and
all-cause mortality using Cox regression models to obtain HRs.
The baseline hazard function of the models was stratified by
center and age, with age categorized as ,25 y, then every 5-y
age group, and $75 y. Significant interactions (PA 3 BMI and
PA 3 WC) were observed with respect to all-cause mortality
(P , 0.005). HRs were therefore estimated within strata defined by BMI (3 groups according to WHO classification) and
WC (2 groups, cutoffs: $102 cm for men and $88 cm for
women). Two sets of covariates were included in the models:
1) sex; 2) sex, lifestyle (alcohol intake and smoking), and
demographic covariates (education). The lifestyle and demographic characteristics selected a priori were education
(none/primary school, technical/professional, secondary, and
longer education), alcohol intake (baseline: 0, .0–6, .6–12,
.12–24, .24–60, and .60 g/d), and smoking (current, former, and never). In addition to the main covariates described
above, potential dietary confounders were evaluated for inclusion in the Cox regression models. However, none of the
dietary variables tested [fiber (g/d), energy (kcal/d), dairy (g/d),
red meat (g/d), and fish (g/d); crude and adjusted for energy
intake by using both the standard and residual methods] yielded
an important change (,10%) in the HR estimates for the PA and
adiposity exposure variables and were therefore not included.
Similarly, stratification of the highest BMI group into 2 groups
(30–34.9 and $35) yielded similar estimates in each group and
are therefore not presented.
We estimated center-specific RRs by comparing levels of
physical inactivity, general and abdominal obesity—adjusted for
sex, education, smoking, and alcohol intake—using binomial
regression. We also estimated RRs adjusted for BMI. Adjusted
RRs were used to calculate the population attributable fraction
(PAF):
RR 2 1
P AF ¼ pd
ð1Þ
RR
Where pd is the proportion of deaths exposed to the risk
factor of interest, and RR is the adjusted RR (20). The STATA
command “punafcc” was used to calculate the PAFs and
95% CIs. PAFs were thereafter combined by using a randomeffect meta-analysis to assess the proportion of mortality that
could have been averted by avoiding the following risk factors: inactivity (all inactive individuals become at least moderately inactive, i.e., moving from category 1 to category 2 or
higher of the Cambridge Index), high BMI [all individuals
classified as obese (BMI $30) become nonobese], and high
WC ($88 cm and $102 cm in women and men, respectively).
These analyses were adjusted for the same covariates used
23.3
15.2
15.5
32.5
17.4
27.0
17.7
17.4
36.3
25.7
28.8
23.9
23.8
25.1
22.4
29.1
24.6
22.9
23.9
24.8
35.9
39.3
38.4
32.1
35.1
33.8
36.2
38.1
28.8
31.2
2
1
12,144
15,099
14,544
27,848
217,181
10,608
9836
9480
24,953
116,980
EPIC, European Prospective Investigation into Cancer and Nutrition.
Values are means 6 SDs.
3
Five-year age-standardized death rates (in the European standard population) were computed for the common age range of 50–69 y.
11.9
21.6
22.3
10.3
25.2
10.3
21.5
21.5
11.1
18.2
5.8
6.0
8.3
7.3
6.3
10.3
10.0
13.9
11.7
11.5
11.4
11.3
14.1
11.8
12.6
6
6
6
6
6
49.2
49.0
57.1
56.7
51.2
13,164
25,562
23,142
14,527
9931
7989
7140
9747
52.2
52.1
58.7
56.5
52.6
57.0
41.0
51.8
53.6
17,099
29,697
24,355
France
Italy
Spain
United Kingdom
General
Health conscious
Netherlands
Greece
Germany
Heidelberg
Potsdam
Sweden
Denmark
Total
12,533
14,763
58.4
43.2
43.0
52.4
8.6
9.3
7.8
4.4
10.3
7.1
8.0
7.0
4.3
9.6
6
6
6
6
6
11.3
11.2
13.9
11.5
11.1
11.1
18.8
39.3
4.0
17.7
24.7
44.2
13.9
19.3
27.4
26.9
16.1
20.6
25.0
24.7
26.7
35.5
38.1
26.3
25.9
27.9
33.8
22.7
26.5
34.2
15.8
7.5
54.0
33.8
16.5
8.5
33.0
7.6
5.3
7.0
4.6
11.1
7.7
8.2
10.9
13.6
12.6
12.9
9.9
6
6
6
6
9.3
12.9
11.0
12.7
6
6
6
6
9.3
12.3
11.2
12.3
12.6
13.5
52.8 6 6.5
50.7 6 8.1
48.3 6 8.3
50.3 6 7.5
50.7 6 7.2
13.1
12.6
12.6
9.3
9.0
9.2
4.2
26.7
21.6
32.9
14.9
12.1
23.8
27.2
41.0
39.1
35.2
36.0
30.0
17.1
36.9
48.5
13.5
21.3
3.6
4.7
5.0
6.8
6.2
F
M
F
M
F
M
F
M
F
M
F
M
EPIC center
Inactive, %
Mortality rate per
1000 person-years3
Mean personyears follow-up
Age at recruitment,2 y
Total participants, n
TABLE 1
Sample size, length of follow-up, age at recruitment, and frequency of total and cause-specific mortality in the EPIC, by country and study center1
A total of 116,980 men (mean age 52.6 y) and 217,181
women (mean age 51.2 y) were included in the current analysis
(Tables 1 and 2; Supplemental Table 1 and Supplemental
Table 2). Across all centers, the mean follow-up time was
12.4 y, corresponding to 4,154,915 person-years. There were
11,086 deaths among men and 10,352 deaths among women.
Within the BMI strata, the hazard of all-cause mortality was
reduced by 20–30% across groups when the moderately inactive individuals were compared with the inactive individuals
(the reference group). In normal-weight and overweight individuals, higher levels of PA were associated with further
reduction in hazards, which were most pronounced in the
normal-weight group, i.e., decreased by 41% in those categorized as active compared with those categorized as inactive.
In contrast, in those with a BMI .30, no further reduction in
hazard was observed with increasing levels of PA beyond that
for the moderately inactive group. Adjustment for additional
covariates did not materially change these estimates (model 2;
Table 3).
Similar results were observed when participants were
stratified according to abdominal adiposity (WC $88 cm and
$102 cm in women and men, respectively). The most pronounced decreased hazard was observed between the inactive
(reference) and moderately inactive groups in both the abdominally lean (HR: 0.75; 95% CI: 0.72, 0.78) and abdominally obese (HR: 0.79; 95% CI: 0.75, 0.84) groups. A further
reduction in hazard across PA groups was observed in abdominally lean but not in abdominally obese groups (Table 3).
Results were similar within subgroups defined by sex, age, and
smoking status (Supplemental Tables 3–5).
Similar to overall activity, higher levels of recreational
activity was associated with lower HRs of all-cause mortality
independent of covariates in each BMI and WC group; however, occupational activity was not related to mortality in
working individuals (Supplemental Table 6).
If all inactive individuals were at least moderately inactive, the number of deaths would theoretically be reduced
by 7.35% (95% CI: 5.88, 8.83; I2: 70.2%; P , 0.001)
(Figure 1A), and life expectancy at birth would increase by
0.70 y (95% CI: 0.56, 0.84). These estimates were only
marginally attenuated by additional adjustment for BMI (I2:
70.7%; PAF: 7.08%; 95% CI: 5.58, 8.58). Comparable estimates for obesity (BMI .30) were lower: 3.66% (95% CI:
2.30, 5.01; I2: 82.5%; P , 0.001; Figure 1B) and 0.34 y
(95% CI: 0.21, 0.48), which suggests that physical inactivity is responsible for more than twice as many deaths
as general obesity in this European population. Finally, we
calculated the PAF and gain in life expectancy for avoiding
Moderately
inactive, %
RESULTS
15.1
12.1
13.7
M
F
Moderately
active, %
Active, %
above. Gain in life expectancy was calculated from life
tables (21).
Analyses within subgroups defined by sex, age group, and
smoking status and a sensitivity analysis that excluded the first
3 y of follow-up (3116 participants were excluded, of whom
2317 were deceased)—to minimize the possibility of reverse
causation due to underlying disease—were performed. All
analyses were performed by using STATA 12 statistical
software (StataCorp LP).
F
EKELUND ET AL.
M
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PHYSICAL ACTIVITY AND MORTALITY
TABLE 2
Anthropometric, lifestyle, and demographic characteristics of the EPIC cohort across levels of physical activity, by sex1
Men
BMI
18.5–24.9 kg/m2
25–29.9 kg/m2
30–34.9 kg/m2
.35 kg/m2
Waist circumference, cm
,88 (F)/,102 (M)
$88 (F)/$102 (M)
Alcohol
0 g/d
.0–6 g/d
.6–12 g/d
.12–24 g/d
.24–60 g/d
.60 g/d
Smoking
Never
Former
Smoker
Education
None/primary school
Technical/professional
Secondary
Longer education
Women
Total N
Inactive,
%
Moderately
inactive, %
Moderately
active, %
Active,
%
Total N
Inactive,
%
Moderately
inactive, %
Moderately
active, %
Active,
%
40,006
58,005
16,290
2,629
28.7
50.2
17.7
3.4
34.4
49.9
13.6
2.2
35.1
49.6
13.4
2.0
37.1
49.1
12.1
1.8
113,216
69,981
25,196
8,788
37.5
36.8
18.2
7.5
54.1
32.0
10.5
3.4
59.5
29.2
8.7
2.6
59.9
29.8
8.0
2.2
89,938
27,042
68.0
32.0
76.6
23.4
78.7
21.3
81.7
18.3
164,928
52,253
63.3
36.8
78.1
21.9
81.9
18.1
82.1
17.8
7646
23,823
18,952
26,002
31,802
8755
10.9
24.0
15.6
19.5
23.2
6.9
5.7
20.0
16.5
23.2
27.7
6.9
5.6
18.9
16.2
22.9
28.7
7.7
5.3
19.7
16.2
22.3
28.0
8.5
37,317
87,963
39,013
32,427
19,064
1,397
30.5
38.2
13.3
11.1
6.4
0.5
15.2
41.0
18.3
15.6
9.3
0.7
11.4
41.4
20.1
16.8
9.7
0.7
9.4
41.6
21.3
16.8
10.1
0.8
36,836
43,575
36,569
28.1
38.0
33.9
32.1
37.6
30.3
32.3
36.9
30.7
32.3
36.7
31.1
124,251
48,851
44,079
63.4
16.5
20.2
57.1
22.8
20.2
55.6
25.1
19.4
50.7
27.4
21.9
41,129
28,908
13,702
33,241
39.9
21.4
12.0
26.8
29.4
22.6
13.6
34.4
35.6
24.8
11.0
28.7
38.4
29.6
9.9
22.1
76,142
54,216
38,295
48,528
54.7
17.2
13.9
14.2
31.9
25.3
18.6
24.2
25.6
26.6
19.8
28.0
25.2
33.5
18.4
23.0
1
EPIC, European Prospective Investigation into Cancer and Nutrition.
high WC (.88 cm and $102 cm in women and men, respectively), and the estimate was similar to that for inactivity:
6.53% (95% CI: 4.90, 8.15) (Figure 1C), corresponding to an
estimated gain in life expectancy of 0.62 y (95% CI: 0.46,
0.79). The combined PAFs and estimated gains in life ex-
pectancy for inactivity and BMI and high WC are shown in
Figure 2A, B and Figure 3A, B. Supplemental Table 7
shows the proportion of deaths exposed to physical inactivity,
general obesity, and abdominal obesity and the respective
adjusted RRs by study center.
TABLE 3
HRs and 95% CIs of all-cause mortality in relation to physical activity levels within strata of BMI and waist circumference groups1
Inactive
Moderately inactive
Moderately active
Active
HR per one-level difference
in physical activity2
8285
8815
4338
1 (reference)
1 (reference)
1 (reference)
0.70 (0.66, 0.74)
0.77 (0.74, 0.82)
0.80 (0.74, 0.87)
0.64 (0.60, 0.69)
0.74 (0.70, 0.79)
0.73 (0.67, 0.81)
0.59 (0.55, 0.63)
0.72 (0.67, 0.77)
0.79 (0.71, 0.87)
0.84 (0.82, 0.86)
0.90 (0.88, 0.92)
0.91 (0.88, 0.94)
8285
8815
4338
1 (reference)
1 (reference)
1 (reference)
0.76 (0.72, 0.81)
0.82 (0.77, 0.86)
0.84 (0.78, 0.91)
0.71 (0.67, 0.76)
0.78 (0.73, 0.83)
0.76 (0.69, 0.84)
0.65 (0.60, 0.70)
0.75 (0.70, 0.80)
0.82 (0.74, 0.90)
0.87 (0.85, 0.89)
0.91 (0.89, 0.93)
0.92 (0.89, 0.95)
14,362
7076
1 (reference)
1 (reference)
0.75 (0.72, 0.78)
0.79 (0.75, 0.84)
0.70 (0.67, 0.74)
0.74 (0.69, 0.80)
0.67 (0.63, 0.70)
0.76 (0.70, 0.82)
0.88 (0.86, 0.89)
0.90 (0.88, 0.92)
14,362
7076
1 (reference)
1 (reference)
0.80 (0.76, 0.83)
0.84 (0.79, 0.89)
0.76 (0.72, 0.79)
0.78 (0.73, 0.84)
0.71 (0.68, 0.75)
0.80 (0.73, 0.86)
0.90 (0.88, 0.91)
0.91 (0.89, 0.94)
Deaths, n
BMI
Model 13
18.5–24.9 kg/m2
25–29.9 kg/m2
.30 kg/m2
Model 24
18.5–24.9 kg/m2
25–29.9 kg/m2
.30 kg/m2
Waist circumference, cm
Model 13
,88 (F)/,102 (M)
$88 (F)/$102 (M)
Model 24
,88 (F)/,102 (M)
$88 (F)/$102 (M)
1
Data were analyzed by Cox regression models.
Physical activity variables entered into the model as an ordinal variable.
3
Model 1: adjusted for sex; stratified by age at recruitment and study center. For waist circumference, sex was included as a stratum variable rather than as
a covariate to meet the proportional hazards assumption.
4
Model 2: adjusted as for model 1 and for education, smoking, and alcohol.
2
6 of 9
EKELUND ET AL.
FIGURE 1 Proportion of deaths averted when all inactivity (lowest category of the Cambridge Index; A), general obesity [BMI (in kg/m2) .30; B], and
abdominal obesity ($88 cm and $102 cm in men and women, respectively; C) were removed. Data were adjusted for age, sex, education, smoking, and
alcohol intake (n = 334,161). PAF, population attributable fraction.
DISCUSSION
PA is inversely associated with all-cause mortality at all levels
of BMI and WC. The greatest reduction in risk was observed
in the comparison of inactive and moderately active groups.
Physical inactivity may theoretically be responsible for twice
as many total deaths as high BMI ($30) in this population,
similar to the number of deaths averted if abdominal adiposity
were eliminated.
FIGURE 2 The combined proportion of number of deaths theoretically averted when all inactivity (lowest category of Cambridge Index) and general
obesity [BMI (in kg/m2) .30] were removed (A) and estimated life expectancy gain when all inactivity and general obesity were avoided (B). Data were
adjusted for age, sex, education, smoking, and alcohol intake (n = 334,161). PAF, population attributable fraction.
PHYSICAL ACTIVITY AND MORTALITY
7 of 9
FIGURE 3 The combined proportion of number of deaths averted when all inactivity (lowest category of Cambridge Index) and abdominal obesity ($88
cm and $102 cm in women and men, respectively) were avoided (A) and estimated life expectancy gain when all inactivity and abdominal obesity were
avoided (B). Data were adjusted for age, sex, education, smoking, and alcohol intake (n = 334,161). PAF, population attributable fraction.
We observed significant interactions between PA and BMI and
WC in relation to all-cause mortality. The most pronounced risk
reductions by increasing levels of PA were observed in those categorized as normal weight and abdominally lean. However, across
all strata for both general and abdominal adiposity, a markedly
reduced hazard was observed between those categorized as inactive
and those categorized as moderately inactive. Data from the United
States suggest that PA reduces but does not eliminate the increased
risk of adiposity on all-cause mortality when cross-classifying
activity and BMI groups (5, 6). Others have shown that exercising
for 15 min/d (defined as the low-volume exercise group) is associated with a 14% reduction in risk of all-cause mortality compared
with inactivity in an Asian population (2). Our results extend these
previous observations to also include European men and women and
suggest that, within each strata for BMI and WC, the hazard of allcause mortality was substantially reduced when the inactive group
was compared with the moderately inactive group. Thus, emerging
evidence is accumulating indicating that substantial health benefits
may be achieved by fairly small increases in PA.
Results from our calibration study suggest that moving from
one category to the next (e.g., from the inactive to the moderately
inactive group) is associated with an increase in PAEE of between
90 and 110 kcal/d in men and women with a similar BMI, as in the
EPIC cohort (16). Assuming that all inactive individuals were
truly inactive and did not participate in moderate and vigorous
intensity, this amount of energy expenditure can be achieved by
an increase in PAEE equivalent to w20 min of brisk walking per
day, which is lower than the current PA recommendations for
public health (22–24).
Approximately 9.2 million deaths occurred in European men
and women in 2008 (25), of which—according to our estimates
from the current study—676,000 deaths may be attributable to
physical inactivity compared with 337,000 deaths attributable to
obesity (BMI .30). Our PAF estimates were derived directly
from the RR estimates in the EPIC population, prevalence data
using measured exposures and based on theoretically avoiding
high BMI ($30), and all physical inactivity (the inactive category from the Cambridge Index)—more likely achievable in
population-wide public health campaigns.
Our estimate of gain in life expectancy for PA is similar to that
from Lee et al. (3) but lower than that from other comparable
studies (2, 4, 26). The differences between studies are likely
explained by differences in the assessment and the prevalence of
the risk factors and RRs between populations, different calculations of PAFs, and differences in subsequent gains in life expectancy. In this cohort, the proportions of people at risk were
22.7% for inactivity, 15.8% for obesity, and 21.9% for high WC.
These prevalence estimates, and hence the estimates of PAF, may
differ between populations. However, the advantage of examining the PAFs for these risk factors within a single cohort using
the observed prevalence estimates is the ability to compare the
relative importance of physical inactivity and central and general
obesity. Our results suggest that the influence of physical inactivity on mortality appears to be greater than that of high BMI
and similar to that of high WC in European men and women.
Our results should be interpreted keeping the following limitations in mind. We observed significant heterogeneity in PAF
estimates across centers. Apart from differences in RR estimates
and the prevalence of risk factors between centers, heterogeneity
was mainly explained by the Greek center (in men), where the
risk of death associated with general and abdominal obesity was
inverse in combination with the highest PAF for physical inactivity; by the French cohort (women only), in which neither
general nor abdominal obesity was associated with an increased
risk of premature death; and by the Spanish center (in women), in
which no association between PA and mortality was observed.
Furthermore, whereas BMI and WC may both be indicators of
total fat mass rather than general and abdominal adiposity, respectively (27), prospective observational studies consistently
suggest a higher risk of mortality associated with high WC than
with high BMI (28–30). From a public health perspective, it is
therefore encouraging that our results suggest that small increases in PA in those who are currently categorized as inactive
appear to be associated with significant reductions in all-cause
mortality at all levels of BMI and WC.
The strengths of this study included its prospective design and
its large number of participants, for whom measured height,
weight, and WC values spanned a wide range. The large sample
size and long follow-up facilitated exclusion of early years of
follow-up in the sensitivity analysis. This minimized the likelihood of confounding resulting from low activity in lean participants with subclinical disease. A broad range of covariates
8 of 9
EKELUND ET AL.
permitted the evaluation of potential confounders, such as diet,
smoking history, and alcohol intake. HRs, PAFs, and gains in life
expectancy were estimated by using a validated measure of PA
(16). Obesity was defined on the basis of measured height,
weight, and WC, which eliminated misclassification bias. Although our global measure of PA has been validated (16), it is still
a relatively imprecise way of characterizing activity. However,
the impact of nondifferential misclassification would be to attenuate the association between activity and mortality. Thus, our
results are more likely to underestimate the true importance of PA
rather than to accentuate it. The same is not true for obesity,
because it is more accurately measured; thus, the estimates of the
association reported here are more likely to reflect the true underlying association.
EPIC Europe is a population-based study, but it was never
intended to be entirely representative of the adult European
population. Representativeness matters considerably in attempts
to generalize prevalence estimates of an exposure or a disease, but
it is much less of an issue in the interpretation of measures of
association in a cohort study, provided the full range of exposures
is observed in the population. Despite not being entirely representative, the prevalence of obesity in our sample is similar to that
reported for European men and women (31). We considered the
possibility of confounding by co-existent disease among participants by adjusting for comorbidities and excluding deaths in
the first 3 y of follow-up. PA and anthropometric measures were
assessed only at baseline; therefore, the current analysis did not take
into consideration changes in PA and anthropometric measures over
time.
The greatest reductions in all-cause mortality risk were observed between the inactive and the moderately inactive groups
across levels of general and abdominal adiposity, which suggests
that efforts to encourage even small increases in activity in inactive individuals may be of public health benefit. The hypothetical number of deaths reduced by avoiding inactivity in this
population may be double that with an approach that avoided high
BMI and similar to that of an approach that avoided high WC.
The authors’ responsibilities were as follows—UE and HAW: conceived
the idea, drafted the manuscript, had full access to all the data in the study,
and took responsibility for the integrity of the data and the accuracy of the
data analysis; HAW and JL: performed the statistical analyses; and UE,
HAW, TN, AMM, EW, SB, SSS and NJW: interpreted the results. KO,
JNØ, A Tjønneland, NFJ, SM, AF, GF, A Trichopoulou, PL, DT, KL, RK,
PF, IL, MJ, MB, HB, DP, SS, SP, RT, PV, PHP, EM, HBB-d-M, JRQ, AA,
M-JS JMH, EA, LA, BH, EW, MS, MJ, TJK, RCT, K-TK, ER (EPIC investigators): conducted the research and were responsible for the study design
and data collection. All authors critically revised the manuscript for intellectual content and approved the final version of the manuscript. The funders
had no role in the design and conduct of the study; collection, management,
analysis, and interpretation of the data; or preparation, review, or approval of
the manuscript. No potential conflicts of interest relevant to this manuscript
were reported.
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